Average treatment effects standardized to treated covariates

att() computes the average treatment effect on the treated.

Usage

att(
  fit,
  newdata = NULL,
  y = NULL,
  type = c("mean", "rmean"),
  cutoff = NULL,
  interval = "credible",
  level = 0.95,
  nsim_mean = 200L,
  show_progress = TRUE
)

Arguments

fit

A "causalmixgpd_causal_fit" object from run_mcmc_causal().

newdata

Ignored for marginal estimands. If supplied, a warning is issued and training data are used.

y

Ignored for marginal estimands. If supplied, a warning is issued and training data are used.

type

Character; type of mean treatment effect:

"mean" (default): ordinary mean ATE
"rmean": restricted-mean ATE (requires cutoff)

cutoff

Finite numeric cutoff for restricted mean; required for type = "rmean", ignored otherwise.

interval

Character or NULL; type of credible interval:

NULL: no interval
"credible" (default): equal-tailed quantile intervals
"hpd": highest posterior density intervals

level

Numeric credible level for intervals (default 0.95 for 95 percent CI).

nsim_mean

Number of posterior predictive draws used by simulation-based mean targets. Ignored for analytical ordinary means.

show_progress

Logical; if TRUE, print step messages and render progress where supported.

Value

An object of class "causalmixgpd_ate" containing the ATT summary, optional intervals, and the arm-specific predictive objects used in the aggregation. The returned object includes a top-level $fit_df data frame for direct extraction.

Details

The estimand is $$\mathrm{ATT} = E\{Y(1) - Y(0) \mid A = 1\},$$ approximated by marginalizing over the empirical covariate distribution of treated units.

Examples

# \donttest{
N <- 25
X <- data.frame(x1 = stats::rnorm(N))
A <- stats::rbinom(N, 1, 0.5)
y <- abs(stats::rnorm(N)) + 0.1
mcmc_small <- list(niter = 100, nburnin = 50, thin = 1, nchains = 1, seed = 1)
cb <- build_causal_bundle(y = y, X = X, A = A, backend = "sb", kernel = "normal",
                         components = 3, mcmc_outcome = mcmc_small, mcmc_ps = mcmc_small)
fit <- run_mcmc_causal(cb, show_progress = FALSE)
#> Defining model
#> Building model
#> Setting data and initial values
#> Checking model sizes and dimensions
#>   [Note] This model is not fully initialized. This is not an error.
#>          To see which variables are not initialized, use model$initializeInfo().
#>          For more information on model initialization, see help(modelInitialization).
#> ===== Monitors =====
#> thin = 1: beta
#> ===== Samplers =====
#> RW sampler (2)
#>   - beta[]  (2 elements)
#> Compiling
#>   [Note] This may take a minute.
#>   [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details.
#> Compiling
#>   [Note] This may take a minute.
#>   [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details.
#> running chain 1...
#> Defining model
#> Building model
#> Setting data and initial values
#> Checking model sizes and dimensions
#>   [Note] This model is not fully initialized. This is not an error.
#>          To see which variables are not initialized, use model$initializeInfo().
#>          For more information on model initialization, see help(modelInitialization).
#> ===== Monitors =====
#> thin = 1: alpha, beta_mean, beta_ps_mean, sd, w
#> ===== Samplers =====
#> conjugate sampler (3)
#>   - sd[]  (3 elements)
#> categorical sampler (11)
#>   - z[]  (11 elements)
#> RW sampler (9)
#>   - alpha
#>   - beta_mean[]  (3 elements)
#>   - beta_ps_mean[]  (3 elements)
#>   - v[]  (2 elements)
#>   [Note] Assigning an RW_block sampler to nodes with very different scales can result in low MCMC efficiency.  If all nodes assigned to RW_block are not on a similar scale, we recommend providing an informed value for the "propCov" control list argument, or using the "barker" sampler instead.
#> Compiling
#>   [Note] This may take a minute.
#>   [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details.
#> Compiling
#>   [Note] This may take a minute.
#>   [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details.
#>   [Warning] To calculate WAIC, set 'WAIC = TRUE', in addition to having enabled WAIC in building the MCMC.
#> running chain 1...
#> Defining model
#> Building model
#> Setting data and initial values
#> Checking model sizes and dimensions
#>   [Note] This model is not fully initialized. This is not an error.
#>          To see which variables are not initialized, use model$initializeInfo().
#>          For more information on model initialization, see help(modelInitialization).
#> ===== Monitors =====
#> thin = 1: alpha, beta_mean, beta_ps_mean, sd, w
#> ===== Samplers =====
#> conjugate sampler (3)
#>   - sd[]  (3 elements)
#> categorical sampler (14)
#>   - z[]  (14 elements)
#> RW sampler (9)
#>   - alpha
#>   - beta_mean[]  (3 elements)
#>   - beta_ps_mean[]  (3 elements)
#>   - v[]  (2 elements)
#>   [Note] Assigning an RW_block sampler to nodes with very different scales can result in low MCMC efficiency.  If all nodes assigned to RW_block are not on a similar scale, we recommend providing an informed value for the "propCov" control list argument, or using the "barker" sampler instead.
#> Compiling
#>   [Note] This may take a minute.
#>   [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details.
#> Compiling
#>   [Note] This may take a minute.
#>   [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details.
#>   [Warning] To calculate WAIC, set 'WAIC = TRUE', in addition to having enabled WAIC in building the MCMC.
#> running chain 1...
att(fit, interval = "credible", nsim_mean = 100)
#> [att] Preparing ATT inputs
#> [att] Preparing propensity-score adjustment
#> [att] Predicting treated-arm effects
#> [att] Predicting control-arm effects
#> [att] Aggregating ATT estimates
#> ATT (Average Treatment Effect on the Treated)
#>   Prediction points: 1
#>   Conditional (covariates): NO
#>   Propensity score used: YES
#>   PS scale: logit
#>   Credible interval: credible (95%)
#> 
#> ATT estimates (treated - control):
#>    mean  lower upper
#>  -0.004 -0.017 0.006
# }